See the attached Appendix 1, which forms part of this application, and which discusses aspects and methods that can be used in some embodiments of the improved invention shown in the attached Figures and described below.
U.S. Pat. No. 4,359,527 to Zetter issued on Nov. 16, 1982 with the title “Cancer diagnostic assay”, and is incorporated herein by reference. U.S. Pat. No. 4,359,527 describes an in vitro cancer diagnostic assay that includes providing a substratum coated with a layer of visible particles susceptible to ingestion by capillary endothelial cells, plating such cells onto the substratum, allowing the cells to adhere, incubating the cells with a test sample, measuring the area of the visible particle-depleted phagokinetic track left by at least one of the cells, and comparing that area to the track area left by a control cell, a comparatively larger test track area indicating the presence in the test sample of a factor associated with cancer cells.
U.S. Pat. No. 4,447,545 to DeFazio et al. issued on May 8, 1984 with the title “Bladder cancer detection”, and is incorporated herein by reference. U.S. Pat. No. 4,447,545 describes a technique for screening populations to detect potential bladder cancer patients. The screening test is based on a discovered correlation between the respective ratios of C-reactive protein to total protein in urine and serum and the incidence of bladder cancer.
U.S. Pat. No. 4,965,725 to Rutenberg issued on Oct. 23, 1990 with the title “Neural network based automated cytological specimen classification system and method”, and is incorporated herein by reference. U.S. Pat. No. 4,965,725 describes an automated screening system and method for cytological specimen classification in which a neural network is utilized in performance of the classification function. Also described is an automated microscope and associated image-processing circuitry.
U.S. Pat. No. 5,677,966 to Doerrer et al. issued on Oct. 14, 1997 with the title “Interactive automated cytology method incorporating both manual and automatic determinations”, and is incorporated herein by reference. U.S. Pat. No. 5,677,966 describes an automated interactive cytology system provides expedited handling of samples, minimizing false negatives, while not substantially increasing the number false positives. A computerized system identifies and displays the cells which are of greatest interest to the cytologist. The system then processes this information on all cells identified to classify the slide as normal, abnormal, or questionable based on a statistical analysis of cells meeting given criteria. Before displaying the results of the statistical analysis, a cytologist reviews the cells which the computer has determined to be most significant. It is only then after the cytologist has determined whether the cells are positive, negative, or questionable, that the determination is inputted into the automated system. The automated system then compares the cytologist's analysis with its own statistical analysis. Based on the two opinions, the cytologist determines how to advise a doctor regarding the sample.
U.S. Pat. No. 5,260,871 to Goldberg issued on Nov. 9, 1993 with the title “Method and apparatus for diagnosis of breast tumors”, and is incorporated herein by reference. U.S. Pat. No. 5,260,871 describes an apparatus for distinguishing benign from malignant tumors in ultrasonic images of candidate tissue taken from a patient. A region of interest is located and defined on the ultrasonic image, including substantially all of the candidate tissue and excluding substantially all the normal tissue. The region of interest is digitized, generating an array of pixels intensity values. A first features is generated from the arrays of pixels corresponding to the angular second moment of the pixel intensity values. A second feature is generated from the array of pixels corresponding to the inverse contrast of the pixel intensity values. A third feature is generated from the array of pixels corresponding to the short run emphasis of the pixel intensity values. The first, second and third feature values are provided to a neural network. A set of trained weights are applied to the feature values, which generates a network output between 0 and 1, whereby the output values tend toward 1 when the candidate tissue is malignant and the output values tend toward 0 when the candidate tissue is benign.
U.S. Pat. No. 5,264,343 to Krystosek et al. issued on Nov. 23, 1993 with the title “Method for distinguishing normal and cancer cells”, and is incorporated herein by reference. U.S. Pat. No. 5,264,343 describes a method of electing the presence or absence of exposed nuclear DNA is described. Cells are reacted with a reaction composition comprising DNA polymerase I, DNase I, and the nucleotides dATP, dCTP, dGTP, and dTTP or dUTP, at least one of said nucleotides being biotin labeled. Biotin labeled nucleotides incorporated in exposed DNA are detected. Also described is a kit useful for detecting the presence or absence of exposed DNA in cells.
U.S. Pat. No. 5,301,681 to DeBan et al. issued on Apr. 12, 1994 with the title “Device for detecting cancerous and precancerous conditions in a breast”, and is incorporated herein by reference. This patent describes a device for detecting and monitoring physiological conditions in mammalian tissue, and method for using the same. The device includes sensors for sensing physiological conditions and generating signals in response thereto and processor operatively associated with the sensors for receiving and manipulating the signals to produce a generalization indicative of normal and abnormal physiological condition of mammalian tissue. The processor is characterized to include a neural network having a predetermined solution spaced memory, the solution space memory including regions indicative of two or more physiological conditions, wherein the generalization is characterized by the signals projected into the regions.
U.S. Pat. No. 5,412,665 to Gruodis et al. issued on May 2, 1995 with the title “Parallel operation linear feedback shift register”, and is incorporated herein by reference. U.S. Pat. No. 5,412,665 describes a parallel operation linear-feedback shift-register (LFSR) that generates random test patterns or creates a signature that represents the response of a device under test at ultra high speed using low speed components and/or a slow rate clock. The apparatus is comprised of: a register connected to an external clock, and a plurality of combinatorial logic networks sequentially connected, the last of which drives the register which in turn feeds back into the first of the combinatorial logic networks. Each of the combinatorial networks provides a pseudo-random pattern, outputted in parallel, thereby creating a high speed data flow. By providing additional data inputs to the combinatorial networks, the pseudo-random patterns become the signature of the input data.
U.S. Pat. No. 5,733,721 to Hemstreet III et al. issued on Mar. 31, 1998 with the title “Cell analysis method using quantitative fluorescence image analysis”, and is incorporated herein by reference. U.S. Pat. No. 5,733,721 describes a system for evaluating one or more biochemical markers for evaluating individual cancer risk, cancer diagnosis and for monitoring therapeutic effectiveness and cancer recurrence, particularly of bladder cancer. The system uses automated quantitative fluorescence image analysis of a cell sample collected from a body organ. Cells are treated with a fixative solution which inhibits crystal formation. Cell images are selected and stored as grey level images for further analysis. Cell images may be corrected for autofluorescence using a novel autofluorescence correction method. A neural net computer may be used to distinguish true-positive images from false-positive images to improve accuracy of cancer risk assessment. Cells having images positive for a marker may be compared to threshold quantities related to predetermined cancer risk.
U.S. Pat. No. 5,983,211 to Heseltine et al. issued on Nov. 9, 1999 with the title “Method and apparatus for the diagnosis of colorectal cancer”, and is incorporated herein by reference. U.S. Pat. No. 5,983,211 describes a process in which cancer of the colon is assessed in a patient. The probabilities of developing cancer involves the initial step of extracting a set of sample body fluids from the patient. Fluids can be evaluated to determine certain marker constituents in the body fluids. Fluids which are extracted have some relationship to me development of cancer, precancer or tendency toward cancerous conditions. The body fluid markers are measured and other quantified. The marker data then is evaluated using a nonlinear technique exemplified through the use of a multiple input and multiple output neural network having a variable learning rate and training rate. The neural network is provided with data from other patients for the same or similar markers. Data from other patients who did and did not have cancer is used in the learning of the neural network which thereby processes the data and provides a determination that the patient has a cancerous condition, precancer cells or a tendency towards cancer.
U.S. Pat. No. 6,125,194 to Yeh et al. issued on Sep. 26, 2000 with the title “Method and system for re-screening nodules in radiological images using multi-resolution”, and is incorporated herein by reference. U.S. Pat. No. 6,125,194 describes an automated detection method and system to improve the diagnostic procedures of radiological images containing abnormalities, such as lung cancer nodules. The detection method and system use a multi-resolution approach to enable the efficient detection of nodules of different sizes, and to further enable the use of a single nodule phantom for correlation and matching in order to detect all or most nodule sizes. The detection method and system use spherical parameters to characterize the nodules, thus enabling a more accurate detection of non-conspicuous nodules. A robust pixel threshold generation technique is applied in order to increase the sensitivity of the system. In addition, the detection method and system increase the sensitivity of true nodule detection by analyzing only the negative cases, and by recommending further re-assessment only of cases determined by the detection method and system to be positive. The detection method and system use multiple classifiers including back propagation neural network, data fusion, decision based pruned neural network, and convolution neural network architecture to generate the classification score for the classification of lung nodules. Such multiple neural network architectures enable the learning of subtle characteristics of nodules to differentiate the nodules from the corresponding anatomic background. A final decision making then selects a portion of films with highly suspicious nodules for further reviewing.
U.S. Pat. No. 6,284,482 to Eisen et al. issued on Sep. 4, 2001 with the title “Method for detection of abnormal keratinization in epithelial tissue”, and is incorporated herein by reference. U.S. Pat. No. 6,284,482 describes an analytical system, including an imaging system, to detect precancerous and cancerous cells. A transepithelial non-lacerational brush produces sufficient cells from all three layers of the epithelium so that an analytical system comprising a programmed computer can detect which cells exhibit abnormal keratinization and require further examination because of a likely suspicion of said pre-cancerous and cancerous conditions. The method and system can apply to the diagnosis non-cancerous conditions as well.
U.S. Pat. No. 6,463,438 to Veltri et al. issued on Oct. 8, 2002 with the title “Neural network for cell image analysis for identification of abnormal cells”, and is incorporated herein by reference. U.S. Pat. No. 6,463,438 describes a neural network is used in a system to detect abnormalities in cells, including cancer in bladder tissue cells. The system has an image analysis system for generating data representative of imaging variables from an image of stained cells. The set of data is provided to a neural network which has been trained to detect abnormalities from known tissue cells with respect to the data from the same set of imaging variables. A conventional sigmoid-activated neural network, or alternatively, a hybrid neural network having a combination of sigmoid, Gaussian and sinusoidal activation functions may be utilized. The trained neural network applies a set of weight factors obtained during training to the data to classify the unknown tissue cell as normal or abnormal.
U.S. Pat. No. 6,553,356 to Good et al. issued on Apr. 22, 2003 with the title “Multi-view computer-assisted diagnosis”, and is incorporated herein by reference. U.S. Pat. No. 6,553,356 describes abnormal regions in living tissue are detected by obtaining images from different views of the living tissue; performing single-image CAD of each image to determine suspected abnormal regions depicted in the image; and combining measurements of the suspected abnormal regions in each image to determine whether a suspected abnormal region is an abnormal region. The living tissue may be a human breast and the abnormal region may be a mass in the breast. Ipsilateral mammographic views of the breast, a craniocaudal view, and a mediolateral oblique view may be used. Features which are relatively invariant or behave predictably with respect to breast compression are extracted using the single-image CAD and then combined.
U.S. Pat. No. 6,962,789 to Bacus issued on Nov. 8, 2005 with the title “Method for quantitating a protein by image analysis”, and is incorporated herein by reference. U.S. Pat. No. 6,962,789 describes a method for determining expression levels of one or a multiplicity of target proteins in a tissue or cell sample.
U.S. Pat. No. 6,996,549 to Zhang et al. issued on Feb. 7, 2006 with the title “Computer-aided image analysis”, and is incorporated herein by reference. U.S. Pat. No. 6,996,549 describes digitized image data that are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.
U.S. Pat. No. 7,155,050 to Sloge et al. issued on Dec. 26, 2006 with the title “Method of analyzing cell samples, by creating and analyzing a resultant image”, and is incorporated herein by reference. U.S. Pat. No. 7,155,050 describes comparing multiple samples of cell extract containing a plurality of components. The method includes the steps of preparing at least two samples of cell extract from at least two groups of cells and of exposing each of said sample of said cell extract to a different one of a set of matched markers, e.g., luminescent markers, to bind the marker to the cell extract to label the cell extract, each marker within said set of markers being capable of binding to the cell extract and can be individually detected from all other markers within said set. The samples are then mixed to form a mixture and said mixture is electrophoresed to separate the components within the cell extract. At least two electronic images of the electrophoresed mixture are obtained (I) by detection of the individual markers, each image being represented by detection of a marker different from the others. One resultant electronic image (Ires) of the obtained at least two electronic images is created (II) and analyzed in order to identify spot analysis areas (III). The identified spot analysis areas are applied on the respective at least two electronic images for evaluating said areas in order to detect spots representing components of said cell extracts (IV).
U.S. Pat. No. 7,760,927 to Gholap et al. issued on Jul. 20, 2010 with the title “Method and system for digital image based tissue independent simultaneous nucleus cytoplasm and membrane quantitation”, and is incorporated herein by reference. U.S. Pat. No. 7,760,927 describes a method and system for automatic digital image based tissue independent simultaneous nucleus, cytoplasm and membrane quantitation. Plural types of pixels including cell components including at least cell cytoplasm and cell membranes from a biological tissue sample to which a chemical compound has been applied and has been processed to remove background pixels and pixels including counterstained components are simultaneously identified. The identified cell components pixels are automatically classified to determine a medical conclusion such as a human breast cancer, a human prostate cancer or an animal cancer.
U.S. Pat. No. 7,979,212 to Gholap et al. issued on Jul. 12, 2011 with the title “Method and system for morphology based mitosis identification and classification of digital images”, and is incorporated herein by reference. U.S. Pat. No. 7,979,212 describes a method and system for morphology-based mitosis identification and classification of digital images. Luminance parameters such as intensity, etc. from a digital image of a biological sample (e.g., tissue cells) to which a chemical compound (e.g., a marker dye) has been applied are analyzed and corrected if necessary. Morphological parameters (e.g., size, elongation ratio, parallelism, boundary roughness, convex hull shape, etc.) from individual components within the biological sample are analyzed. A medical conclusion (e.g., type and count of mitotic cells) or a life science and biotechnology experiment conclusion is determined from the analyzed luminance and morphological parameters. The method and system may be used to develop applications for automatically obtaining a medical diagnosis (e.g., a carcinoma diagnosis).
U.S. Pat. No. 8,064,679 to Griffin issued on Nov. 22, 2011 with the title “Targeted edge detection method and apparatus for cytological image processing applications”, and is incorporated herein by reference. This U.S. Pat. No. 8,064,679 describes that edges in cytological image data are identified by obtaining a digital image of a specimen and computing a gradient image from the obtained digital image. A scaling function is applied to the grayscale image to identify regions of interest (e.g., edges of cell nuclei) in the digital image. Edges of the regions of interest are then identified based on the product of the computed gradient image and the scaling image. The scaling function may be applied to each image frame and one or more scaling thresholds are established for each frame to selectively pass, suppress, or scale pixels based on their measured intensity values. The scaled image resulting from application of the scaling function is multiplied with the gradient image to produce a targeted gradient image that identifies the edges of the region of interest. The targeted gradient image isolates edges corresponding to particular cellular structures, while rejecting other edges within the image.
U.S. Pat. No. 8,642,349 to Yeatman et al. issued on Feb. 4, 2014 with the title “Artificial neural network proteomic tumor classification”, and is incorporated herein by reference. U.S. Pat. No. 8,642,349 describes a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction.
U.S. Pat. No. 8,644,582 to Yoshihara et al. issued on Feb. 4, 2014 with the title “Support system for histopathological diagnosis, support program for histopathological diagnosis”, and is incorporated herein by reference. U.S. Pat. No. 8,644,582 describes a support system for histopathological diagnosis includes a cell nucleus uniformity evaluation unit evaluating a uniformity of a plurality of cell nuclei included in a ductal region in an image. With this configuration, there is provided a support system, a support method and a support program for histopathological diagnosis, which enables realization of highly accurate cancer differentiation in a pathological diagnosis.
United States Patent publication US2011/0081087 by Moore published on Apr. 7, 2011 with the title “Fast Hysteresis Thresholding in Canny Edge Detection”, and is incorporated herein by reference. Patent publication US2011/0081087 describes a method of image processing that includes non-recursive hysteresis thresholding in Canny edge detection. The non-recursive hysteresis thresholding reduces computational complexity and eliminates the potential for call stack overflow. More specifically, hysteresis thresholding is performed in a raster-scan order pass over the image data to connect edge segments to form continuous edges.
This in addition, the following U.S. patent publications discuss aspects and methods that can be used in some embodiments of the invention: US2002/0001586, US2004/0043436, US2006/0036372, US2006/0084125, US2007/0099207, US2009/0252728, US2009/0317836, US2009/0326359, US2010/0086932, US2010/0111396, US2010/0119128, US2010/0128950, US2010/0172568, US2010/0323903, US2011/0282819, US2012/0052063, US2012/0082362, US2012/0177280, US2013/0071876, US2014/0080731, and US2014/0139625, each of which is hereby incorporated herein by reference in its entirety for all purposes.
What is needed is an improved method for automatically detecting abnormal cells and for automatically distinguishing normal cells from cancerous cells and diagnosing and treating cancers.